Land cover classification using multitemporal chris/proba imagesand multitemporal texture

Huiran Jin, Peijun Li, Wenjie Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Most existing multitemporal classification researches use spectral information alone. However, adding spatial structure and temporal correlation in the classification could improve the classification accuracy. This paper proposed a new method to extract multitemporal texture by the Pseudo Cross Variogram (PCV). The derived texture features were combined with the original spectral information for multitemporal classification. The performance of the proposed multitemporal texture was evaluated in land cover classification using bi-temporal hyperspectral CHRIS/PRBOA images. The experiments showed that CHIRS/PROBA data is applicable in multitemporal classification, and including multitemporal texture in multitemporal classification could lead to a significant increase in overall classification accuracy, compared to the classification using spectral information alone.

Original languageEnglish (US)
Title of host publication2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
PagesIV742-IV745
Edition1
DOIs
StatePublished - Dec 1 2008
Externally publishedYes
Event2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings - Boston, MA, United States
Duration: Jul 6 2008Jul 11 2008

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Number1
Volume4

Other

Other2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
Country/TerritoryUnited States
CityBoston, MA
Period7/6/087/11/08

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

Keywords

  • CHRIS
  • Multitemporal classification
  • Multitemporal texture
  • Pseudo-cross variogram

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